
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput

def a__       (			A_,	A_=0.999,	A_="cosine",	):






  '''simple docstring'''





  if alpha_transform_type == "cosine":

    def alpha_bar_fn(A_   ):
      return math.cos((t + 0.008) / 1.008 * math.pi / 2   ) ** 2

  elif alpha_transform_type == "exp":

    def alpha_bar_fn(A_   ):
      return math.exp(t * -12.0   )

  else:
    raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}'''   )

  __magic_name__								=			[]
  for i in range(A_   ):
    __magic_name__								=			i / num_diffusion_timesteps
    __magic_name__								=			(i + 1) / num_diffusion_timesteps
    betas.append(min(1 - alpha_bar_fn(A_   ) / alpha_bar_fn(A_   ),	A_   )   )
  return torch.tensor(A_,	dtype=torch.floataa   )





class 			UpperCAmelCase_     (					_A    ,			_A	):


  '''simple docstring'''







  a__       			=						[e.name for e in KarrasDiffusionSchedulers]
  a__       			=						2
  @register_to_config
  def __init__(					self     :					List[Any]     ,				UpperCamelCase__     :					int = 1000     ,				UpperCamelCase__     :					float = 0.00085     ,				UpperCamelCase__     :					float = 0.012     ,				UpperCamelCase__     :					str = "linear"     ,				UpperCamelCase__     :					Optional[Union[np.ndarray, List[float]]] = None     ,				UpperCamelCase__     :					str = "epsilon"     ,				UpperCamelCase__     :					Optional[bool] = False     ,				UpperCamelCase__     :					Optional[bool] = False     ,				UpperCamelCase__     :					float = 1.0     ,				UpperCamelCase__     :					str = "linspace"     ,				UpperCamelCase__     :					int = 0     ,				)     ->      Optional[Any]:







    """simple docstring"""






    if trained_betas is not None:
      __magic_name__								=			torch.tensor(UpperCamelCase__     ,				dtype=torch.floataa       )
    elif beta_schedule == "linear":
      __magic_name__								=			torch.linspace(UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				dtype=torch.floataa       )
    elif beta_schedule == "scaled_linear":
      # this schedule is very specific to the latent diffusion model.
      __magic_name__								=			(
          torch.linspace(beta_start**0.5     ,				beta_end**0.5     ,				UpperCamelCase__     ,				dtype=torch.floataa       ) ** 2
      )
    elif beta_schedule == "squaredcos_cap_v2":
      # Glide cosine schedule
      __magic_name__								=			betas_for_alpha_bar(UpperCamelCase__     ,				alpha_transform_type="""cosine"""       )
    elif beta_schedule == "exp":
      __magic_name__								=			betas_for_alpha_bar(UpperCamelCase__     ,				alpha_transform_type="""exp"""       )
    else:
      raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''       )

    __magic_name__								=			1.0 - self.betas
    __magic_name__								=			torch.cumprod(self.alphas     ,				dim=0       )

    #  set all values
    self.set_timesteps(UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__       )
    __magic_name__								=			use_karras_sigmas
  def 				_lowercase				(					self     :					int     ,				UpperCamelCase__     :					Union[str, Any]     ,				UpperCamelCase__     :					Optional[Any]=None       )     ->      List[str]:







    """simple docstring"""






    if schedule_timesteps is None:
      __magic_name__								=			self.timesteps

    __magic_name__								=			(schedule_timesteps == timestep).nonzero()

    # The sigma index that is taken for the **very** first `step`
    # is always the second index (or the last index if there is only 1)
    # This way we can ensure we don't accidentally skip a sigma in
    # case we start in the middle of the denoising schedule (e.g. for image-to-image)
    if len(self._index_counter       ) == 0:
      __magic_name__								=			1 if len(UpperCamelCase__       ) > 1 else 0
    else:
      __magic_name__								=			timestep.cpu().item() if torch.is_tensor(UpperCamelCase__       ) else timestep
      __magic_name__								=			self._index_counter[timestep_int]

    return indices[pos].item()
  @property
  def 				_lowercase				(					self     :					Optional[Any]       )     ->      Any:







    """simple docstring"""






    if self.config.timestep_spacing in ["linspace", "trailing"]:
      return self.sigmas.max()

    return (self.sigmas.max() ** 2 + 1) ** 0.5
  def 				_lowercase				(					self     :					Optional[int]     ,				UpperCamelCase__     :					torch.FloatTensor     ,				UpperCamelCase__     :					Union[float, torch.FloatTensor]     ,				)     ->      torch.FloatTensor:







    """simple docstring"""






    __magic_name__								=			self.index_for_timestep(UpperCamelCase__       )

    __magic_name__								=			self.sigmas[step_index]
    __magic_name__								=			sample / ((sigma**2 + 1) ** 0.5)
    return sample
  def 				_lowercase				(					self     :					str     ,				UpperCamelCase__     :					int     ,				UpperCamelCase__     :					Union[str, torch.device] = None     ,				UpperCamelCase__     :					Optional[int] = None     ,				)     ->      Dict:







    """simple docstring"""






    __magic_name__								=			num_inference_steps

    __magic_name__								=			num_train_timesteps or self.config.num_train_timesteps

    # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
    if self.config.timestep_spacing == "linspace":
      __magic_name__								=			np.linspace(0     ,				num_train_timesteps - 1     ,				UpperCamelCase__     ,				dtype=UpperCamelCase__       )[::-1].copy()
    elif self.config.timestep_spacing == "leading":
      __magic_name__								=			num_train_timesteps // self.num_inference_steps
      # creates integer timesteps by multiplying by ratio
      # casting to int to avoid issues when num_inference_step is power of 3
      __magic_name__								=			(np.arange(0     ,				UpperCamelCase__       ) * step_ratio).round()[::-1].copy().astype(UpperCamelCase__       )
      timesteps += self.config.steps_offset
    elif self.config.timestep_spacing == "trailing":
      __magic_name__								=			num_train_timesteps / self.num_inference_steps
      # creates integer timesteps by multiplying by ratio
      # casting to int to avoid issues when num_inference_step is power of 3
      __magic_name__								=			(np.arange(UpperCamelCase__     ,				0     ,				-step_ratio       )).round().copy().astype(UpperCamelCase__       )
      timesteps -= 1
    else:
      raise ValueError(
          F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''       )

    __magic_name__								=			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5       )
    __magic_name__								=			np.log(UpperCamelCase__       )
    __magic_name__								=			np.interp(UpperCamelCase__     ,				np.arange(0     ,				len(UpperCamelCase__       )       )     ,				UpperCamelCase__       )

    if self.config.use_karras_sigmas:
      __magic_name__								=			self._convert_to_karras(in_sigmas=UpperCamelCase__     ,				num_inference_steps=self.num_inference_steps       )
      __magic_name__								=			np.array([self._sigma_to_t(UpperCamelCase__     ,				UpperCamelCase__       ) for sigma in sigmas]       )

    __magic_name__								=			np.concatenate([sigmas, [0.0]]       ).astype(np.floataa       )
    __magic_name__								=			torch.from_numpy(UpperCamelCase__       ).to(device=UpperCamelCase__       )
    __magic_name__								=			torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2       ), sigmas[-1:]]       )

    __magic_name__								=			torch.from_numpy(UpperCamelCase__       )
    __magic_name__								=			torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2       )]       )

    if str(UpperCamelCase__       ).startswith("""mps"""       ):
      # mps does not support float64
      __magic_name__								=			timesteps.to(UpperCamelCase__     ,				dtype=torch.floataa       )
    else:
      __magic_name__								=			timesteps.to(device=UpperCamelCase__       )

    # empty dt and derivative
    __magic_name__								=			None
    __magic_name__								=			None

    # for exp beta schedules, such as the one for `pipeline_shap_e.py`
    # we need an index counter
    __magic_name__								=			defaultdict(UpperCamelCase__       )
  def 				_lowercase				(					self     :					Optional[Any]     ,				UpperCamelCase__     :					Optional[int]     ,				UpperCamelCase__     :					Dict       )     ->      Dict:







    """simple docstring"""






    __magic_name__								=			np.log(UpperCamelCase__       )

    # get distribution
    __magic_name__								=			log_sigma - log_sigmas[:, np.newaxis]

    # get sigmas range
    __magic_name__								=			np.cumsum((dists >= 0)     ,				axis=0       ).argmax(axis=0       ).clip(max=log_sigmas.shape[0] - 2       )
    __magic_name__								=			low_idx + 1

    __magic_name__								=			log_sigmas[low_idx]
    __magic_name__								=			log_sigmas[high_idx]

    # interpolate sigmas
    __magic_name__								=			(low - log_sigma) / (low - high)
    __magic_name__								=			np.clip(UpperCamelCase__     ,				0     ,				1       )

    # transform interpolation to time range
    __magic_name__								=			(1 - w) * low_idx + w * high_idx
    __magic_name__								=			t.reshape(sigma.shape       )
    return t
  def 				_lowercase				(					self     :					Dict     ,				UpperCamelCase__     :					torch.FloatTensor     ,				UpperCamelCase__     :					Tuple       )     ->      torch.FloatTensor:







    """simple docstring"""






    __magic_name__								=			in_sigmas[-1].item()
    __magic_name__								=			in_sigmas[0].item()

    __magic_name__								=			7.0  # 7.0 is the value used in the paper
    __magic_name__								=			np.linspace(0     ,				1     ,				UpperCamelCase__       )
    __magic_name__								=			sigma_min ** (1 / rho)
    __magic_name__								=			sigma_max ** (1 / rho)
    __magic_name__								=			(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
    return sigmas
  @property
  def 				_lowercase				(					self     :					Optional[Any]       )     ->      List[Any]:







    """simple docstring"""






    return self.dt is None
  def 				_lowercase				(					self     :					str     ,				UpperCamelCase__     :					Union[torch.FloatTensor, np.ndarray]     ,				UpperCamelCase__     :					Union[float, torch.FloatTensor]     ,				UpperCamelCase__     :					Union[torch.FloatTensor, np.ndarray]     ,				UpperCamelCase__     :					bool = True     ,				)     ->      Union[SchedulerOutput, Tuple]:







    """simple docstring"""






    __magic_name__								=			self.index_for_timestep(UpperCamelCase__       )

    # advance index counter by 1
    __magic_name__								=			timestep.cpu().item() if torch.is_tensor(UpperCamelCase__       ) else timestep
    self._index_counter[timestep_int] += 1

    if self.state_in_first_order:
      __magic_name__								=			self.sigmas[step_index]
      __magic_name__								=			self.sigmas[step_index + 1]
    else:
      # 2nd order / Heun's method
      __magic_name__								=			self.sigmas[step_index - 1]
      __magic_name__								=			self.sigmas[step_index]

    # currently only gamma=0 is supported. This usually works best anyways.
    # We can support gamma in the future but then need to scale the timestep before
    # passing it to the model which requires a change in API
    __magic_name__								=			0
    __magic_name__								=			sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

    # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
    if self.config.prediction_type == "epsilon":
      __magic_name__								=			sigma_hat if self.state_in_first_order else sigma_next
      __magic_name__								=			sample - sigma_input * model_output
    elif self.config.prediction_type == "v_prediction":
      __magic_name__								=			sigma_hat if self.state_in_first_order else sigma_next
      __magic_name__								=			model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
          sample / (sigma_input**2 + 1)
      )
    elif self.config.prediction_type == "sample":
      __magic_name__								=			model_output
    else:
      raise ValueError(
          F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''       )

    if self.config.clip_sample:
      __magic_name__								=			pred_original_sample.clamp(
          -self.config.clip_sample_range     ,				self.config.clip_sample_range       )

    if self.state_in_first_order:
      # 2. Convert to an ODE derivative for 1st order
      __magic_name__								=			(sample - pred_original_sample) / sigma_hat
      # 3. delta timestep
      __magic_name__								=			sigma_next - sigma_hat

      # store for 2nd order step
      __magic_name__								=			derivative
      __magic_name__								=			dt
      __magic_name__								=			sample
    else:
      # 2. 2nd order / Heun's method
      __magic_name__								=			(sample - pred_original_sample) / sigma_next
      __magic_name__								=			(self.prev_derivative + derivative) / 2

      # 3. take prev timestep & sample
      __magic_name__								=			self.dt
      __magic_name__								=			self.sample

      # free dt and derivative
      # Note, this puts the scheduler in "first order mode"
      __magic_name__								=			None
      __magic_name__								=			None
      __magic_name__								=			None

    __magic_name__								=			sample + derivative * dt

    if not return_dict:
      return (prev_sample,)

    return SchedulerOutput(prev_sample=UpperCamelCase__       )
  def 				_lowercase				(					self     :					Tuple     ,				UpperCamelCase__     :					torch.FloatTensor     ,				UpperCamelCase__     :					torch.FloatTensor     ,				UpperCamelCase__     :					torch.FloatTensor     ,				)     ->      torch.FloatTensor:







    """simple docstring"""






    __magic_name__								=			self.sigmas.to(device=original_samples.device     ,				dtype=original_samples.dtype       )
    if original_samples.device.type == "mps" and torch.is_floating_point(UpperCamelCase__       ):
      # mps does not support float64
      __magic_name__								=			self.timesteps.to(original_samples.device     ,				dtype=torch.floataa       )
      __magic_name__								=			timesteps.to(original_samples.device     ,				dtype=torch.floataa       )
    else:
      __magic_name__								=			self.timesteps.to(original_samples.device       )
      __magic_name__								=			timesteps.to(original_samples.device       )

    __magic_name__								=			[self.index_for_timestep(UpperCamelCase__     ,				UpperCamelCase__       ) for t in timesteps]

    __magic_name__								=			sigmas[step_indices].flatten()
    while len(sigma.shape       ) < len(original_samples.shape       ):
      __magic_name__								=			sigma.unsqueeze(-1       )

    __magic_name__								=			original_samples + noise * sigma
    return noisy_samples





  def __len__(					self     :					Any       )     ->      Dict:







    """simple docstring"""






    return self.config.num_train_timesteps



from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCAmelCase     :	Dict       			=  logging.get_logger(__name__)

__lowerCAmelCase     :	int       			=  {
    'microsoft/trocr-base-handwritten': (
        'https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}




class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  a__       			=						"""trocr"""
  a__       			=						["""past_key_values"""]
  a__       			=						{
      """num_attention_heads""": """decoder_attention_heads""",
      """hidden_size""": """d_model""",
      """num_hidden_layers""": """decoder_layers""",
  }
  def __init__(					self     :					Dict     ,				UpperCamelCase__     :					Optional[Any]=5_0265     ,				UpperCamelCase__     :					int=1024     ,				UpperCamelCase__     :					int=12     ,				UpperCamelCase__     :					List[Any]=16     ,				UpperCamelCase__     :					Union[str, Any]=4096     ,				UpperCamelCase__     :					List[Any]="gelu"     ,				UpperCamelCase__     :					Optional[int]=512     ,				UpperCamelCase__     :					Optional[Any]=0.1     ,				UpperCamelCase__     :					int=0.0     ,				UpperCamelCase__     :					str=0.0     ,				UpperCamelCase__     :					Optional[int]=2     ,				UpperCamelCase__     :					List[str]=0.02     ,				UpperCamelCase__     :					Dict=0.0     ,				UpperCamelCase__     :					List[str]=True     ,				UpperCamelCase__     :					str=False     ,				UpperCamelCase__     :					List[str]=True     ,				UpperCamelCase__     :					Tuple=True     ,				UpperCamelCase__     :					str=1     ,				UpperCamelCase__     :					Union[str, Any]=0     ,				UpperCamelCase__     :					List[str]=2     ,				**UpperCamelCase__     :					Tuple     ,				)     ->      Dict:







    """simple docstring"""






    __magic_name__								=			vocab_size
    __magic_name__								=			d_model
    __magic_name__								=			decoder_layers
    __magic_name__								=			decoder_attention_heads
    __magic_name__								=			decoder_ffn_dim
    __magic_name__								=			activation_function
    __magic_name__								=			max_position_embeddings
    __magic_name__								=			dropout
    __magic_name__								=			attention_dropout
    __magic_name__								=			activation_dropout
    __magic_name__								=			init_std
    __magic_name__								=			decoder_layerdrop
    __magic_name__								=			use_cache
    __magic_name__								=			scale_embedding
    __magic_name__								=			use_learned_position_embeddings
    __magic_name__								=			layernorm_embedding

    super().__init__(
        pad_token_id=UpperCamelCase__     ,				bos_token_id=UpperCamelCase__     ,				eos_token_id=UpperCamelCase__     ,				decoder_start_token_id=UpperCamelCase__     ,				**UpperCamelCase__     ,				)

